Loading data
data1 <- read_csv("data/200820092010.csv", col_names=F, locale = locale(encoding = "ISO-8859-1"))Rows: 25410 Columns: 8── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): X1, X2, X3, X4, X5
dbl (3): X6, X7, X8
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(data1) <- c('useless1', 'useless2', 'useless3', 'province', 'origin', 'd2008', 'd2009', 'd2010')
#guess_encoding("data/200820092010.csv")
#guess_encoding("data/menage.csv")
#guess_encoding("data/incomedk.xlsx")
#guess_encoding("data/CRIMERAW.csv")
data2 <- read_csv("data/201120122013.csv", col_names=F, locale = locale(encoding = "ISO-8859-1"))Rows: 25410 Columns: 8── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): X1, X2, X3, X4, X5
dbl (3): X6, X7, X8
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(data2) <- c('useless11', 'useless21', 'useless31', 'province', 'origin', 'd2011', 'd2012', 'd2013')
data3 <- read_csv("data/201420152016.csv", col_names=F, locale = locale(encoding = "ISO-8859-1"))Rows: 25410 Columns: 8── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): X1, X2, X3, X4, X5
dbl (3): X6, X7, X8
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(data3) <- c('useless12', 'useless22', 'useless32', 'province', 'origin', 'd2014', 'd2015', 'd2016')
data4 <- read_csv("data/201720182019.csv", col_names=F, locale = locale(encoding = "ISO-8859-1"))Rows: 25410 Columns: 8── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): X1, X2, X3, X4, X5
dbl (3): X6, X7, X8
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(data4) <- c('useless13', 'useless23', 'useless33', 'province', 'origin', 'd2017', 'd2018', 'd2019')
data5 <- read_csv("data/202020212022.csv", col_names=F, locale = locale(encoding = "ISO-8859-1"))Rows: 25410 Columns: 8── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): X1, X2, X3, X4, X5
dbl (3): X6, X7, X8
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(data5) <- c('useless14', 'useless24', 'useless34', 'province', 'origin', 'd2020', 'd2021', 'd2022')
data6 <- read_csv("data/202320242025.csv", col_names=F, locale = locale(encoding = "ISO-8859-1"))Rows: 25410 Columns: 8── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): X1, X2, X3, X4, X5
dbl (3): X6, X7, X8
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(data6) <- c('useless15', 'useless25', 'useless35', 'province', 'origin', 'd2023', 'd2024', 'd2025')
data7 <- read_csv("data/CRIMERAW.csv", col_names=F, locale = locale(encoding = "ISO-8859-1"))Rows: 106 Columns: 19── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): X1, X2
dbl (17): X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(data7) <- c('useless151', 'province', 'c2008', 'c2009', 'c2010', 'c2011', 'c2012', 'c2013', 'c2014', 'c2015', 'c2016', 'c2017', 'c2018', 'c2019', 'c2020', 'c2021', 'c2022', 'c2023', 'c2024')
data8 <- read_delim("data/menage.csv", col_names=F, delim=';', locale = locale(encoding = "ISO-8859-1"))Rows: 2080 Columns: 21── Column specification ────────────────────────────────────────────────────────
Delimiter: ";"
chr (4): X1, X2, X3, X4
dbl (17): X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16, X17, X18, X...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.colnames(data8) <- c('useless151', 'age', 'sex', 'province', 't2008', 't2009', 't2010', 't2011', 't2012', 't2013', 't2014', 't2015', 't2016', 't2017', 't2018', 't2019', 't2020', 't2021', 't2022', 't2023', 't2024')
incomedk <- read_xlsx('data/incomedk.xlsx')
incomedk <- incomedk[2:110, ]
colnames(incomedk) <- c('province', 'i2000', 'i2001', 'i2002', 'i2003', 'i2004', 'i2005', 'i2006', 'i2007', 'i2008', 'i2009', 'i2010', 'i2011', 'i2012', 'i2013', 'i2014', 'i2015', 'i2016', 'i2017', 'i2018', 'i2019', 'i2020', 'i2021', 'i2022', 'i2023')
incomedk <- incomedk %>% filter(!province=='All Denmark' | !province=='Bornholm')
areadk <- read_excel("data/areadk.xlsx")New names:areadk$province <- areadk$...1
areadk[, 2:20] <- lapply(areadk[, 2:20], as.numeric)Warning: NAs introduced by coercionWarning: NAs introduced by coercionWarning: NAs introduced by coercionWarning: NAs introduced by coercionareadk[areadk$province == "Christiansø", 2:20] <- 0.4
#head(areadk)Calculating sex ratios and average ages.
#head(data8)
data8 <- data8 %>%
mutate(age_mid = case_when(
str_detect(age, "0-9") ~ 4.5,
str_detect(age, "10-19") ~ 14.5,
str_detect(age, "20-29") ~ 24.5,
str_detect(age, "30-39") ~ 34.5,
str_detect(age, "40-49") ~ 44.5,
str_detect(age, "50-59") ~ 54.5,
str_detect(age, "60-69") ~ 64.5,
str_detect(age, "70-79") ~ 74.5,
str_detect(age, "80-89") ~ 84.5,
str_detect(age, "90+") ~ 95,
TRUE ~ NA_real_
))
#unique(data8$province)
data_long <- data8 %>%
pivot_longer(cols = starts_with("t"),
names_to = "year",
names_prefix = "t",
values_to = "population")
#unique(data_long$province)
avg_age_by_province_year <- data_long %>%
filter(!is.na(age_mid) & sex=='Total') %>%
group_by(province, year) %>%
summarise(
avg_age = weighted.mean(age_mid, population, na.rm = TRUE),
.groups = "drop"
)
#unique(avg_age_by_province_year$province)
pop_by_sex <- data_long %>%
filter(sex %in% c("Total", "Men")) %>%
group_by(province, year, sex) %>%
summarise(total_pop = sum(population, na.rm = TRUE), .groups = "drop")
#unique(pop_by_sex$province)
pop_wide <- pop_by_sex %>%
pivot_wider(names_from = sex, values_from = total_pop)
pop_wide <- pop_wide %>%
mutate(percent_male = (Men / Total) * 100)
long_area <- areadk %>%
mutate(across(`2007`:`2025`, as.numeric)) %>% # convert all years to numeric
pivot_longer(
cols = `2007`:`2025`,
names_to = "year",
values_to = "area"
) %>%
mutate(year = as.character(year))
popdens <- full_join(long_area, pop_wide, by=c('year', 'province'))
popdens$density <- popdens$Total/popdens$area
popdens_wide <- popdens %>%
pivot_wider(
id_cols = province,
names_from = year,
values_from = density,
names_glue = "dens{year}"
)
sexratio <- pop_wide %>% select(province, year, percent_male)Fusing different datasets and creating variables
setequal(unique(data1$province), unique(data2$province))[1] TRUEsetequal(unique(data1$province), unique(data2$province))[1] TRUEdemdata <- left_join(data1, data2, by = c("province", "origin"))
setequal(unique(demdata$province), unique(data3$province))[1] TRUEdemdata <- left_join(demdata, data3, by = c("province", "origin"))
setequal(unique(demdata$province), unique(data4$province))[1] TRUEdemdata <- left_join(demdata, data4, by = c("province", "origin"))
setequal(unique(demdata$province), unique(data5$province))[1] TRUEdemdata <- left_join(demdata, data5, by = c("province", "origin"))
setequal(unique(demdata$province), unique(data6$province))[1] TRUEdemdata <- left_join(demdata, data6, by = c("province", "origin"))
demdata <- demdata %>% select('province', 'origin', 'd2008', 'd2009', 'd2010', 'd2011', 'd2012', 'd2013', 'd2014', 'd2015', 'd2016', 'd2017', 'd2018', 'd2019', 'd2020', 'd2021', 'd2022', 'd2023', 'd2024')
##########
data7 <- data7 %>% filter(!province=='Not stated municipality')
demtotals <- demdata %>% filter(origin=='Total') %>% select('province', 'd2008', 'd2009', 'd2010', 'd2011', 'd2012', 'd2013', 'd2014', 'd2015', 'd2016', 'd2017', 'd2018', 'd2019', 'd2020', 'd2021', 'd2022', 'd2023', 'd2024')
setequal(unique(demtotals$province), unique(data7$province))[1] TRUEsetdiff(unique(demtotals$province), unique(data7$province)) character(0)setdiff(unique(data7$province), unique(demtotals$province)) character(0)crimedata <- left_join(data7, demtotals, by = c("province"))
crimedata <- crimedata %>% filter(!province=='All Denmark')
years <- 2008:2024
for (year in years) {
c_col <- paste0("c", year)
d_col <- paste0("d", year)
rate_col <- paste0("crime_rate", year)
crimedata[[rate_col]] <- crimedata[[c_col]] / crimedata[[d_col]]*1000
}Recoding country names and removing rows labelled ‘All Denmark’ (which is not an actual province).
demdata2 <- demdata %>% filter(!origin=='Total') %>% filter(!province=='All Denmark')
demdata2$origin_alpha3 <- countrycode(demdata2$origin, origin = 'country.name', destination='iso3c')Warning: Some values were not matched unambiguously: Abu Dhabi, Africa not stated, Asia not stated, British West Indies, Czechoslovakia, Dubai, East Jerusalem, Eswantini, Europe not stated, French territories in the Pacific, French West Indies, GDR, Kosovo, Middle East not stated, Netherlands Antilles, North America not stated, North Yemen, Northern Ireland, Not stated, Pacific Islands, Serbia and Montenegro, Sikkim, South and central America not stated, Southwest Africa, Spanish territories in Africa, Stateless, West Indies, Yugoslavia, Yugoslavia, Federal Republicdemdata2$origin_alpha3[demdata2$origin=='Czechoslovakia'] <- 'CZE'
demdata2$origin_alpha3[demdata2$origin=='Dubai'] <- 'UAE'
demdata2$origin_alpha3[demdata2$origin=='Eswantini'] <- 'SWZ'
demdata2$origin_alpha3[demdata2$origin=='Kosovo'] <- 'KSV'
demdata2$origin_alpha3[demdata2$origin=='Netherlands Antilles'] <- 'ANT'
demdata2$origin_alpha3[demdata2$origin=='North Yemen'] <- 'YEM'
demdata2$origin_alpha3[demdata2$origin=='Northern Ireland'] <- 'GBR'
demdata2$stock <- 'Foreign'
demdata2$stock[demdata2$origin=='Denmark'] <- 'Danish'
demdata3 <- demdata2 %>% filter(!province=='All Denmark')Checking coding for origin/stock
test <- demdata3 %>% select('origin', 'stock') %>% unique()
#test
Calculating stock by province
ddgroup <- demdata3 %>% group_by(stock, province) %>% summarise(d2008=sum(d2008), d2009=sum(d2009), d2010=sum(d2010), d2011=sum(d2011), d2012=sum(d2012), d2013=sum(d2013), d2014=sum(d2014), d2015=sum(d2015), d2016=sum(d2016), d2017=sum(d2017), d2018=sum(d2018), d2019=sum(d2019), d2020=sum(d2020), d2021=sum(d2021), d2022=sum(d2022), d2023=sum(d2023), d2024=sum(d2024))`summarise()` has grouped output by 'stock'. You can override using the `.groups` argument.#ddgroupCalculating percentages for demographic groups
years <- 2008:2024
d_cols <- paste0("d", years)
pct_cols <- paste0("pct", years)
demdata_pct <- ddgroup %>%
group_by(province) %>%
mutate(across(all_of(d_cols),
.fns = list,
.names = "temp_{.col}")) %>%
mutate(across(all_of(d_cols),
~ . / sum(., na.rm = TRUE) * 100,
.names = "pct{.col}")) %>%
ungroup()
demdata_pct <- demdata_pct %>%
select(-starts_with("temp_"))
#head(demdata_pct)
############
demdata_wide <- demdata_pct %>%
pivot_longer(
cols = matches("^d\\d{4}$|^pctd\\d{4}$"),
names_to = c("type", "year"),
names_pattern = "^(pct)?d(\\d{4})$"
) %>%
mutate(
type = ifelse(type == "", "count", "pct"),
year = as.integer(year)
) %>%
unite("varname", stock, type, year, sep = "_") %>%
pivot_wider(
names_from = varname,
values_from = value
)Converting to wide format
age_wide <- avg_age_by_province_year %>%
pivot_wider(
names_from = year,
values_from = avg_age,
names_prefix = "a"
)
sex_wide <- sexratio %>%
pivot_wider(
names_from = year,
values_from = percent_male,
names_prefix = "m"
)Fusing various types of data
setequal(unique(crimedata$province), unique(demdata_wide$province))[1] TRUEcrosssec <- full_join(crimedata, demdata_wide, by = c("province"))
setequal(unique(crosssec$province), unique(age_wide$province))[1] TRUEcrosssec <- full_join(crosssec, age_wide, by = c("province"))
setequal(unique(crosssec$province), unique(sex_wide$province))[1] TRUEcrosssec <- full_join(crosssec, sex_wide, by = c("province"))
setequal(unique(crosssec$province), unique(popdens$province))[1] TRUEcrosssec <- full_join(crosssec, popdens_wide, by = c("province"))Logging density
crosssec <- full_join(crosssec, incomedk, by = c("province"))
crosssec <- crosssec %>%
mutate(across(starts_with("dens"), log))Sanity check
lr <- lm(data=crosssec, crime_rate2008 ~ `Foreign_pct_2008` + a2008 + i2008 + m2008 + dens2008)
summary(lr)
Call:
lm(formula = crime_rate2008 ~ Foreign_pct_2008 + a2008 + i2008 +
m2008 + dens2008, data = crosssec)
Residuals:
Min 1Q Median 3Q Max
-10.251 -2.508 -0.291 2.232 11.742
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.3288744 54.4550052 0.92 0.35779
Foreign_pct_2008 0.5468589 0.1422263 3.84 0.00022 ***
a2008 -0.9889064 0.2677577 -3.69 0.00038 ***
i2008 -0.0000294 0.0000094 -3.13 0.00238 **
m2008 0.1381042 0.9157310 0.15 0.88045
dens2008 1.1941775 0.7287009 1.64 0.10468
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.16 on 92 degrees of freedom
(17 observations deleted due to missingness)
Multiple R-squared: 0.598, Adjusted R-squared: 0.577
F-statistic: 27.4 on 5 and 92 DF, p-value: <0.0000000000000002Long format + conversions
crosssec_long <- crosssec %>%
pivot_longer(
cols = matches("^(i|m|a|crime_rate|Danish_pct|Foreign_pct|dens)_?\\d{4}$"),
names_to = c(".value", "year"),
names_pattern = "^(i|m|a|crime_rate|Danish_pct|Foreign_pct|dens)_?(\\d{4})$"
) %>%
mutate(
year = as.integer(year)
) %>%
arrange(province, year) %>% select(province, year, i, m, a, crime_rate, Danish_pct, Foreign_pct, dens) %>% filter(year > 2007 & year < 2024)
crosssec$i_diff_2023_2008 <- crosssec$i2023 - crosssec$i2008
crosssec$m_diff_2023_2008 <- crosssec$m2023 - crosssec$m2008
crosssec$a_diff_2023_2008 <- crosssec$a2023 - crosssec$a2008
crosssec$crime_rate_diff_2023_2008 <- crosssec$crime_rate2023 - crosssec$crime_rate2008
crosssec$Danish_pct_diff_2023_2008 <- crosssec$Danish_pct_2023 - crosssec$Danish_pct_2008
crosssec$Foreign_pct_diff_2023_2008 <- crosssec$Foreign_pct_2023 - crosssec$Foreign_pct_2008
crosssec$dens_diff_2023_2008 <- crosssec$dens2023 - crosssec$dens2008
All regressions + tests
########################DID
lr <- lm(data=crosssec, crime_rate_diff_2023_2008 ~ Foreign_pct_diff_2023_2008 + dens_diff_2023_2008 + a_diff_2023_2008 + i_diff_2023_2008 + m_diff_2023_2008)
summary(lr)
Call:
lm(formula = crime_rate_diff_2023_2008 ~ Foreign_pct_diff_2023_2008 +
dens_diff_2023_2008 + a_diff_2023_2008 + i_diff_2023_2008 +
m_diff_2023_2008, data = crosssec)
Residuals:
Min 1Q Median 3Q Max
-13.784 -2.169 -0.161 1.509 11.935
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.70228958 2.48875847 -1.89 0.062 .
Foreign_pct_diff_2023_2008 -0.26794051 0.17619221 -1.52 0.132
dens_diff_2023_2008 -5.97901679 6.78906771 -0.88 0.381
a_diff_2023_2008 -0.09057873 0.45581807 -0.20 0.843
i_diff_2023_2008 0.00000973 0.00000622 1.56 0.121
m_diff_2023_2008 1.04624377 1.15432287 0.91 0.367
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.98 on 92 degrees of freedom
(17 observations deleted due to missingness)
Multiple R-squared: 0.0804, Adjusted R-squared: 0.0304
F-statistic: 1.61 on 5 and 92 DF, p-value: 0.166regression_std_df <- lm_to_std_df(lr)
write.csv(regression_std_df, 'regs/fulldiff.csv')##########
##########
############Province effects
lr2 <- plm(crime_rate ~ i + m + a + Foreign_pct + dens,
data = crosssec_long,
index = c("province", "year"),
model = "within")
summary(lr2)Oneway (individual) effect Within Model
Call:
plm(formula = crime_rate ~ i + m + a + Foreign_pct + dens, data = crosssec_long,
model = "within", index = c("province", "year"))
Balanced Panel: n = 98, T = 16, N = 1568
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-17.61 -1.53 -0.25 1.26 48.31
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
i -0.00000214 0.00000340 -0.63 0.5300
m -1.09324219 0.58917851 -1.86 0.0637 .
a -1.02607866 0.17135458 -5.99 0.0000000027 ***
Foreign_pct -0.27135753 0.09523211 -2.85 0.0044 **
dens -22.81127769 4.15703690 -5.49 0.0000000480 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Total Sum of Squares: 26300
Residual Sum of Squares: 20000
R-Squared: 0.239
Adj. R-Squared: 0.186
F-statistic: 91.9181 on 5 and 1465 DF, p-value: <0.0000000000000002regression_std_df <- plm_to_std_df(lr2)
write.csv(regression_std_df, 'regs/proveffects.csv')############Year effects (factor)
lr3 <- plm(crime_rate ~ i + m + a + Foreign_pct + factor(year) + dens,
data = crosssec_long,
index = c("province", "year"),
model = "within")
summary(lr3)Oneway (individual) effect Within Model
Call:
plm(formula = crime_rate ~ i + m + a + Foreign_pct + factor(year) +
dens, data = crosssec_long, model = "within", index = c("province",
"year"))
Balanced Panel: n = 98, T = 16, N = 1568
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-17.67 -1.45 -0.24 1.13 47.99
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
i 0.00001248 0.00000408 3.06 0.00224 **
m -0.51765140 0.59297359 -0.87 0.38282
a 0.08847085 0.26590404 0.33 0.73940
Foreign_pct 0.02372272 0.12402690 0.19 0.84834
factor(year)2009 0.62450128 0.51598604 1.21 0.22636
factor(year)2010 -0.98019085 0.54801073 -1.79 0.07388 .
factor(year)2011 -0.94615637 0.58082911 -1.63 0.10354
factor(year)2012 -0.93966870 0.62517099 -1.50 0.13304
factor(year)2013 -3.23132298 0.68541686 -4.71 0.00000265740 ***
factor(year)2014 -2.39523874 0.75281159 -3.18 0.00150 **
factor(year)2015 -4.05912422 0.84191609 -4.82 0.00000157631 ***
factor(year)2016 -4.29171903 0.94497437 -4.54 0.00000604476 ***
factor(year)2017 -4.41675513 1.05125020 -4.20 0.00002814157 ***
factor(year)2018 -4.43831176 1.13944143 -3.90 0.00010 ***
factor(year)2019 -4.43150461 1.22699865 -3.61 0.00031 ***
factor(year)2020 -5.14242313 1.31430578 -3.91 0.00009551448 ***
factor(year)2021 -9.06278101 1.41277955 -6.41 0.00000000019 ***
factor(year)2022 -7.55987113 1.47567298 -5.12 0.00000034116 ***
factor(year)2023 -6.73376899 1.61596531 -4.17 0.00003268344 ***
dens -15.12907609 4.32099808 -3.50 0.00048 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Total Sum of Squares: 26300
Residual Sum of Squares: 18400
R-Squared: 0.3
Adj. R-Squared: 0.243
F-statistic: 31.0466 on 20 and 1450 DF, p-value: <0.0000000000000002regression_std_df <- plm_to_std_df(lr3)
regression_std_dfwrite.csv(regression_std_df, 'regs/provyeareffects.csv')robust_results <- lmtest::coeftest(lr3,
vcov = vcovHC(lr3,
type = "sss",
cluster = "group"))
print(robust_results)
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
i 0.00001248 0.00000564 2.21 0.02714 *
m -0.51765140 0.87190601 -0.59 0.55280
a 0.08847085 0.38111174 0.23 0.81646
Foreign_pct 0.02372272 0.18000052 0.13 0.89517
factor(year)2009 0.62450128 0.28552070 2.19 0.02888 *
factor(year)2010 -0.98019085 0.41213509 -2.38 0.01752 *
factor(year)2011 -0.94615637 0.47045961 -2.01 0.04450 *
factor(year)2012 -0.93966870 0.57905539 -1.62 0.10486
factor(year)2013 -3.23132298 0.71922295 -4.49 0.0000076 ***
factor(year)2014 -2.39523874 0.96508543 -2.48 0.01318 *
factor(year)2015 -4.05912422 1.04866457 -3.87 0.00011 ***
factor(year)2016 -4.29171903 1.20763229 -3.55 0.00039 ***
factor(year)2017 -4.41675513 1.30729530 -3.38 0.00075 ***
factor(year)2018 -4.43831176 1.42890149 -3.11 0.00193 **
factor(year)2019 -4.43150461 1.50215678 -2.95 0.00323 **
factor(year)2020 -5.14242313 1.69645737 -3.03 0.00248 **
factor(year)2021 -9.06278101 1.89577945 -4.78 0.0000019 ***
factor(year)2022 -7.55987113 1.96702140 -3.84 0.00013 ***
factor(year)2023 -6.73376899 2.11933567 -3.18 0.00152 **
dens -15.12907609 4.92912387 -3.07 0.00219 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1dk_results <- lmtest::coeftest(lr3, vcov = vcovSCC(lr3))
print(dk_results)
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
i 0.0000125 0.0000047 2.65 0.00806 **
m -0.5176514 0.3822774 -1.35 0.17591
a 0.0884709 0.3180494 0.28 0.78092
Foreign_pct 0.0237227 0.1265714 0.19 0.85135
factor(year)2009 0.6245013 0.0860024 7.26 0.000000000000623 ***
factor(year)2010 -0.9801908 0.1292252 -7.59 0.000000000000059 ***
factor(year)2011 -0.9461564 0.1958876 -4.83 0.000001509243643 ***
factor(year)2012 -0.9396687 0.2628024 -3.58 0.00036 ***
factor(year)2013 -3.2313230 0.3421342 -9.44 < 0.0000000000000002 ***
factor(year)2014 -2.3952387 0.4324472 -5.54 0.000000036111106 ***
factor(year)2015 -4.0591242 0.5374146 -7.55 0.000000000000075 ***
factor(year)2016 -4.2917190 0.6679802 -6.42 0.000000000178496 ***
factor(year)2017 -4.4167551 0.7633298 -5.79 0.000000008809459 ***
factor(year)2018 -4.4383118 0.8570843 -5.18 0.000000255271210 ***
factor(year)2019 -4.4315046 0.9222962 -4.80 0.000001709244025 ***
factor(year)2020 -5.1424231 0.9697915 -5.30 0.000000131847855 ***
factor(year)2021 -9.0627810 1.0200672 -8.88 < 0.0000000000000002 ***
factor(year)2022 -7.5598711 1.1032760 -6.85 0.000000000010718 ***
factor(year)2023 -6.7337690 1.1971346 -5.62 0.000000022238889 ***
dens -15.1290761 5.1932194 -2.91 0.00363 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1####################
lr <- lm(data=crosssec, 'crime_rate2023 ~ Foreign_pct_2023 + dens2023 + i2023 + a2023 + m2023')
summary(lr)
Call:
lm(formula = "crime_rate2023 ~ Foreign_pct_2023 + dens2023 + i2023 + a2023 + m2023",
data = crosssec)
Residuals:
Min 1Q Median 3Q Max
-10.532 -2.623 -0.321 1.692 18.039
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.85343416 54.52278812 0.02 0.9875
Foreign_pct_2023 0.08529522 0.09626347 0.89 0.3779
dens2023 2.09844713 0.73074644 2.87 0.0051 **
i2023 -0.00001312 0.00000511 -2.57 0.0119 *
a2023 -0.35472498 0.21949163 -1.62 0.1095
m2023 0.45165734 0.92153052 0.49 0.6252
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.14 on 92 degrees of freedom
(17 observations deleted due to missingness)
Multiple R-squared: 0.467, Adjusted R-squared: 0.438
F-statistic: 16.1 on 5 and 92 DF, p-value: 0.0000000000225lr <- lm(data=crosssec, 'crime_rate2023 ~ Foreign_pct_2023 + dens2023 + i2023')
summary(lr)
Call:
lm(formula = "crime_rate2023 ~ Foreign_pct_2023 + dens2023 + i2023",
data = crosssec)
Residuals:
Min 1Q Median 3Q Max
-10.41 -2.67 -0.10 1.80 18.47
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.68430987 2.23539431 2.99 0.0036 **
Foreign_pct_2023 0.14527562 0.09104860 1.60 0.1139
dens2023 2.19844140 0.48776766 4.51 0.000019 ***
i2023 -0.00001280 0.00000491 -2.61 0.0107 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.2 on 94 degrees of freedom
(17 observations deleted due to missingness)
Multiple R-squared: 0.439, Adjusted R-squared: 0.421
F-statistic: 24.5 on 3 and 94 DF, p-value: 0.00000000000847lr <- lm(data=crosssec, 'crime_rate2023 ~ Foreign_pct_2023 + dens2023')
summary(lr)
Call:
lm(formula = "crime_rate2023 ~ Foreign_pct_2023 + dens2023",
data = crosssec)
Residuals:
Min 1Q Median 3Q Max
-12.57 -2.68 -0.03 2.22 19.06
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.0755 1.7695 1.74 0.0852 .
Foreign_pct_2023 0.2789 0.0884 3.15 0.0021 **
dens2023 1.4115 0.4442 3.18 0.0020 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.42 on 101 degrees of freedom
(11 observations deleted due to missingness)
Multiple R-squared: 0.392, Adjusted R-squared: 0.38
F-statistic: 32.5 on 2 and 101 DF, p-value: 0.0000000000125lr <- lm(data=crosssec_long %>% filter(!dens==Inf), 'crime_rate ~ Foreign_pct + dens + i + a + m + as.factor(year) + as.factor(province)')
summary(lr)
Call:
lm(formula = "crime_rate ~ Foreign_pct + dens + i + a + m + as.factor(year) + as.factor(province)",
data = crosssec_long %>% filter(!dens == Inf))
Residuals:
Min 1Q Median 3Q Max
-17.67 -1.45 -0.24 1.13 47.99
Coefficients:
Estimate Std. Error t value
(Intercept) 106.17373769 42.79884099 2.48
Foreign_pct 0.02372272 0.12402690 0.19
dens -15.12907609 4.32099808 -3.50
i 0.00001248 0.00000408 3.06
a 0.08847085 0.26590404 0.33
m -0.51765140 0.59297359 -0.87
as.factor(year)2009 0.62450128 0.51598604 1.21
as.factor(year)2010 -0.98019085 0.54801073 -1.79
as.factor(year)2011 -0.94615637 0.58082911 -1.63
as.factor(year)2012 -0.93966870 0.62517099 -1.50
as.factor(year)2013 -3.23132298 0.68541686 -4.71
as.factor(year)2014 -2.39523874 0.75281159 -3.18
as.factor(year)2015 -4.05912422 0.84191609 -4.82
as.factor(year)2016 -4.29171903 0.94497437 -4.54
as.factor(year)2017 -4.41675513 1.05125020 -4.20
as.factor(year)2018 -4.43831176 1.13944143 -3.90
as.factor(year)2019 -4.43150461 1.22699865 -3.61
as.factor(year)2020 -5.14242313 1.31430578 -3.91
as.factor(year)2021 -9.06278101 1.41277955 -6.41
as.factor(year)2022 -7.55987113 1.47567298 -5.12
as.factor(year)2023 -6.73376899 1.61596531 -4.17
as.factor(province)Aalborg 13.43381507 4.46130072 3.01
as.factor(province)Aarhus 37.88026938 9.63432593 3.93
as.factor(province)Ærø -13.37098175 2.49180728 -5.37
as.factor(province)Albertslund 46.26387013 12.16614686 3.80
as.factor(province)Allerød 28.80494491 7.45405729 3.86
as.factor(province)Assens -5.66297831 1.95698404 -2.89
as.factor(province)Ballerup 45.00316008 13.21482902 3.41
as.factor(province)Billund -7.88678655 1.86713078 -4.22
as.factor(province)Bornholm -9.10225310 1.99029305 -4.57
as.factor(province)Brøndby 50.42314678 13.84318396 3.64
as.factor(province)Brønderslev -12.00888109 1.73499897 -6.92
as.factor(province)Copenhagen 87.29750013 19.12934426 4.56
as.factor(province)Dragør 22.92088284 10.94609198 2.09
as.factor(province)Egedal 12.73913442 7.03623905 1.81
as.factor(province)Esbjerg 10.32647354 3.71907214 2.78
as.factor(province)Faaborg-Midtfyn -6.66524713 1.99958955 -3.33
as.factor(province)Fanø -14.73099670 1.69735579 -8.68
as.factor(province)Favrskov -6.55401674 2.07498684 -3.16
as.factor(province)Faxe -2.46008357 2.22589976 -1.11
as.factor(province)Fredensborg 15.62082244 7.41736677 2.11
as.factor(province)Fredericia 28.27757373 7.72407440 3.66
as.factor(province)Frederiksberg 74.64221844 21.87789484 3.41
as.factor(province)Frederikshavn -0.24757676 2.56771405 -0.10
as.factor(province)Frederikssund 7.91714968 4.80941325 1.65
as.factor(province)Furesø 26.12128368 10.15636010 2.57
as.factor(province)Gentofte 47.51778888 16.05275106 2.96
as.factor(province)Gladsaxe 50.91078995 15.62842715 3.26
as.factor(province)Glostrup 54.94175804 13.82220935 3.97
as.factor(province)Greve 35.19199946 10.87696923 3.24
as.factor(province)Gribskov 5.33558782 4.23315234 1.26
as.factor(province)Guldborgsund -2.79169304 1.61642216 -1.73
as.factor(province)Haderslev -3.82225456 1.39530034 -2.74
as.factor(province)Halsnæs 15.01995583 6.45991888 2.33
as.factor(province)Hedensted -6.29582408 2.05094538 -3.07
as.factor(province)Helsingør 29.23930389 9.16661680 3.19
as.factor(province)Herlev 55.69853861 15.14079373 3.68
as.factor(province)Herning -5.75258353 1.48797039 -3.87
as.factor(province)Hillerød 14.34257298 5.38923094 2.66
as.factor(province)Hjørring -5.59035584 1.57462054 -3.55
as.factor(province)Høje-Taastrup 34.26467796 9.52862449 3.60
as.factor(province)Holbæk 3.15623454 3.01252373 1.05
as.factor(province)Holstebro -5.19005117 1.56952341 -3.31
as.factor(province)Horsens 10.01016805 4.05733047 2.47
as.factor(province)Hørsholm 29.10568252 11.13307108 2.61
as.factor(province)Hvidovre 52.93567851 14.99246451 3.53
as.factor(province)Ikast-Brande -1.57157505 1.68522400 -0.93
as.factor(province)Ishøj 41.37499405 10.66753385 3.88
as.factor(province)Jammerbugt -14.36134945 2.14281537 -6.70
as.factor(province)Kalundborg -1.80171905 2.07158089 -0.87
as.factor(province)Kerteminde -2.68219963 3.13411881 -0.86
as.factor(province)Køge 16.62127445 5.47326456 3.04
as.factor(province)Kolding 10.27553456 3.64774925 2.82
as.factor(province)Læsø -38.29502698 5.01633457 -7.63
as.factor(province)Langeland -15.95968652 1.86844786 -8.54
as.factor(province)Lejre -3.22708025 2.96198528 -1.09
as.factor(province)Lemvig -20.33335799 2.26862530 -8.96
as.factor(province)Lolland -3.37385910 1.61466464 -2.09
as.factor(province)Lyngby-Taarbæk 42.61114532 13.04175950 3.27
as.factor(province)Mariagerfjord -8.94188584 1.63216576 -5.48
as.factor(province)Middelfart 2.49273744 3.44585370 0.72
as.factor(province)Morsø -10.88597906 1.64991342 -6.60
as.factor(province)Næstved 3.50839926 3.06166539 1.15
as.factor(province)Norddjurs -8.83201370 1.68997657 -5.23
as.factor(province)Nordfyns -8.68376182 1.64371999 -5.28
as.factor(province)Nyborg 4.12839059 3.04130197 1.36
as.factor(province)Odder -5.73180202 2.42275833 -2.37
as.factor(province)Odense 35.98865674 9.53381439 3.77
as.factor(province)Odsherred 1.30206727 2.88270145 0.45
as.factor(province)Randers 7.36939651 3.28856912 2.24
as.factor(province)Rebild -15.55706552 2.34048738 -6.65
as.factor(province)Ringkøbing-Skjern -18.30101689 2.61680238 -6.99
as.factor(province)Ringsted 4.42168715 2.61760732 1.69
as.factor(province)Rødovre 59.55062638 16.49190160 3.61
as.factor(province)Roskilde 22.98583646 7.79359323 2.95
as.factor(province)Rudersdal 26.00608007 10.63123180 2.45
as.factor(province)Samsø -25.51682982 2.42082176 -10.54
as.factor(province)Silkeborg -0.96903349 2.52923424 -0.38
as.factor(province)Skanderborg 0.59976960 3.48876869 0.17
as.factor(province)Skive -7.92473937 1.72126625 -4.60
as.factor(province)Slagelse 11.45870849 3.52803272 3.25
as.factor(province)Solrød 23.28774686 9.14078372 2.55
as.factor(province)Sønderborg 5.39133219 4.05171290 1.33
as.factor(province)Sorø -1.04904406 2.37460278 -0.44
as.factor(province)Stevns -7.29393505 2.41622384 -3.02
as.factor(province)Struer -5.25320462 2.14925704 -2.44
as.factor(province)Svendborg 4.21238665 3.79584112 1.11
as.factor(province)Syddjurs -9.67451430 1.47111390 -6.58
as.factor(province)Tårnby 50.10449186 9.76901485 5.13
as.factor(province)Thisted -16.57641277 2.29494327 -7.22
as.factor(province)Tønder -17.15443366 3.30476838 -5.19
as.factor(province)Vallensbæk 40.25376150 13.52497275 2.98
as.factor(province)Varde -14.95563432 2.44637236 -6.11
as.factor(province)Vejen -10.33213438 1.80230270 -5.73
as.factor(province)Vejle 2.41966185 2.31859708 1.04
as.factor(province)Vesthimmerlands -10.20966922 1.86697872 -5.47
as.factor(province)Viborg -8.02167359 1.56683870 -5.12
as.factor(province)Vordingborg -3.92336724 1.80709417 -2.17
Pr(>|t|)
(Intercept) 0.01322 *
Foreign_pct 0.84834
dens 0.00048 ***
i 0.00224 **
a 0.73940
m 0.38282
as.factor(year)2009 0.22636
as.factor(year)2010 0.07388 .
as.factor(year)2011 0.10354
as.factor(year)2012 0.13304
as.factor(year)2013 0.000002657404201 ***
as.factor(year)2014 0.00150 **
as.factor(year)2015 0.000001576311532 ***
as.factor(year)2016 0.000006044762984 ***
as.factor(year)2017 0.000028141566679 ***
as.factor(year)2018 0.00010 ***
as.factor(year)2019 0.00031 ***
as.factor(year)2020 0.000095514484690 ***
as.factor(year)2021 0.000000000190343 ***
as.factor(year)2022 0.000000341157048 ***
as.factor(year)2023 0.000032683440395 ***
as.factor(province)Aalborg 0.00265 **
as.factor(province)Aarhus 0.000088289281803 ***
as.factor(province)Ærø 0.000000093614282 ***
as.factor(province)Albertslund 0.00015 ***
as.factor(province)Allerød 0.00012 ***
as.factor(province)Assens 0.00386 **
as.factor(province)Ballerup 0.00068 ***
as.factor(province)Billund 0.000025494096198 ***
as.factor(province)Bornholm 0.000005209373642 ***
as.factor(province)Brøndby 0.00028 ***
as.factor(province)Brønderslev 0.000000000006687 ***
as.factor(province)Copenhagen 0.000005454649773 ***
as.factor(province)Dragør 0.03644 *
as.factor(province)Egedal 0.07042 .
as.factor(province)Esbjerg 0.00556 **
as.factor(province)Faaborg-Midtfyn 0.00088 ***
as.factor(province)Fanø < 0.0000000000000002 ***
as.factor(province)Favrskov 0.00162 **
as.factor(province)Faxe 0.26925
as.factor(province)Fredensborg 0.03538 *
as.factor(province)Fredericia 0.00026 ***
as.factor(province)Frederiksberg 0.00066 ***
as.factor(province)Frederikshavn 0.92320
as.factor(province)Frederikssund 0.09994 .
as.factor(province)Furesø 0.01021 *
as.factor(province)Gentofte 0.00313 **
as.factor(province)Gladsaxe 0.00115 **
as.factor(province)Glostrup 0.000073876691473 ***
as.factor(province)Greve 0.00124 **
as.factor(province)Gribskov 0.20772
as.factor(province)Guldborgsund 0.08437 .
as.factor(province)Haderslev 0.00623 **
as.factor(province)Halsnæs 0.02020 *
as.factor(province)Hedensted 0.00218 **
as.factor(province)Helsingør 0.00145 **
as.factor(province)Herlev 0.00024 ***
as.factor(province)Herning 0.00012 ***
as.factor(province)Hillerød 0.00787 **
as.factor(province)Hjørring 0.00040 ***
as.factor(province)Høje-Taastrup 0.00033 ***
as.factor(province)Holbæk 0.29495
as.factor(province)Holstebro 0.00097 ***
as.factor(province)Horsens 0.01373 *
as.factor(province)Hørsholm 0.00903 **
as.factor(province)Hvidovre 0.00043 ***
as.factor(province)Ikast-Brande 0.35120
as.factor(province)Ishøj 0.00011 ***
as.factor(province)Jammerbugt 0.000000000029329 ***
as.factor(province)Kalundborg 0.38459
as.factor(province)Kerteminde 0.39225
as.factor(province)Køge 0.00243 **
as.factor(province)Kolding 0.00491 **
as.factor(province)Læsø 0.000000000000041 ***
as.factor(province)Langeland < 0.0000000000000002 ***
as.factor(province)Lejre 0.27611
as.factor(province)Lemvig < 0.0000000000000002 ***
as.factor(province)Lolland 0.03684 *
as.factor(province)Lyngby-Taarbæk 0.00111 **
as.factor(province)Mariagerfjord 0.000000050493214 ***
as.factor(province)Middelfart 0.46955
as.factor(province)Morsø 0.000000000058293 ***
as.factor(province)Næstved 0.25202
as.factor(province)Norddjurs 0.000000198369786 ***
as.factor(province)Nordfyns 0.000000146481713 ***
as.factor(province)Nyborg 0.17485
as.factor(province)Odder 0.01812 *
as.factor(province)Odense 0.00017 ***
as.factor(province)Odsherred 0.65156
as.factor(province)Randers 0.02518 *
as.factor(province)Rebild 0.000000000042244 ***
as.factor(province)Ringkøbing-Skjern 0.000000000004075 ***
as.factor(province)Ringsted 0.09139 .
as.factor(province)Rødovre 0.00032 ***
as.factor(province)Roskilde 0.00324 **
as.factor(province)Rudersdal 0.01456 *
as.factor(province)Samsø < 0.0000000000000002 ***
as.factor(province)Silkeborg 0.70168
as.factor(province)Skanderborg 0.86353
as.factor(province)Skive 0.000004506668691 ***
as.factor(province)Slagelse 0.00119 **
as.factor(province)Solrød 0.01095 *
as.factor(province)Sønderborg 0.18352
as.factor(province)Sorø 0.65872
as.factor(province)Stevns 0.00258 **
as.factor(province)Struer 0.01464 *
as.factor(province)Svendborg 0.26730
as.factor(province)Syddjurs 0.000000000067129 ***
as.factor(province)Tårnby 0.000000330786848 ***
as.factor(province)Thisted 0.000000000000818 ***
as.factor(province)Tønder 0.000000239094750 ***
as.factor(province)Vallensbæk 0.00297 **
as.factor(province)Varde 0.000000001251559 ***
as.factor(province)Vejen 0.000000012003988 ***
as.factor(province)Vejle 0.29685
as.factor(province)Vesthimmerlands 0.000000053361475 ***
as.factor(province)Viborg 0.000000347152154 ***
as.factor(province)Vordingborg 0.03009 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.57 on 1450 degrees of freedom
(96 observations deleted due to missingness)
Multiple R-squared: 0.754, Adjusted R-squared: 0.734
F-statistic: 38 on 117 and 1450 DF, p-value: <0.0000000000000002-15.09932769*sd(crosssec_long$dens, na.rm=T)/sd(crosssec_long$crime_rate, na.rm=T)[1] -2.8759regression_std_df <- lm_to_std_df(lr)Warning: longer object length is not a multiple of shorter object lengthWarning: longer object length is not a multiple of shorter object lengthregression_std_dfwrite.csv(regression_std_df, 'regs/fullcross.csv')lr2 <- lm(data=crosssec_long %>% filter(!dens==Inf), 'crime_rate ~ Foreign_pct + as.factor(year) + as.factor(province)')
summary(lr2)
Call:
lm(formula = "crime_rate ~ Foreign_pct + as.factor(year) + as.factor(province)",
data = crosssec_long %>% filter(!dens == Inf))
Residuals:
Min 1Q Median 3Q Max
-17.92 -1.42 -0.25 1.13 48.28
Coefficients:
Estimate Std. Error t value
(Intercept) 27.3345 1.3735 19.90
Foreign_pct -0.2198 0.1010 -2.18
as.factor(year)2009 0.7089 0.4870 1.46
as.factor(year)2010 -0.5374 0.4893 -1.10
as.factor(year)2011 -0.3875 0.4927 -0.79
as.factor(year)2012 -0.2177 0.4978 -0.44
as.factor(year)2013 -2.3108 0.5049 -4.58
as.factor(year)2014 -1.3004 0.5182 -2.51
as.factor(year)2015 -2.8036 0.5380 -5.21
as.factor(year)2016 -2.8745 0.5744 -5.00
as.factor(year)2017 -2.7789 0.6041 -4.60
as.factor(year)2018 -2.6583 0.6293 -4.22
as.factor(year)2019 -2.4205 0.6500 -3.72
as.factor(year)2020 -2.8514 0.6611 -4.31
as.factor(year)2021 -6.4674 0.6747 -9.59
as.factor(year)2022 -4.9142 0.7067 -6.95
as.factor(year)2023 -3.7000 0.7765 -4.77
as.factor(province)Aalborg -4.1037 1.2752 -3.22
as.factor(province)Aarhus 1.9805 1.2780 1.55
as.factor(province)Ærø -16.6342 1.3880 -11.98
as.factor(province)Albertslund 4.9439 1.9732 2.51
as.factor(province)Allerød 3.6558 1.3223 2.76
as.factor(province)Assens -11.2125 1.4293 -7.84
as.factor(province)Ballerup -0.8727 1.2633 -0.69
as.factor(province)Billund -4.5170 1.2656 -3.57
as.factor(province)Bornholm -14.0058 1.4219 -9.85
as.factor(province)Brøndby 5.2917 2.1686 2.44
as.factor(province)Brønderslev -12.5872 1.4285 -8.81
as.factor(province)Christiansø -24.0605 1.4706 -16.36
as.factor(province)Copenhagen 18.2720 1.6661 10.97
as.factor(province)Dragør -13.4478 1.3324 -10.09
as.factor(province)Egedal -12.2991 1.3220 -9.30
as.factor(province)Esbjerg -3.2719 1.2716 -2.57
as.factor(province)Faaborg-Midtfyn -12.2797 1.4203 -8.65
as.factor(province)Fanø -13.4867 1.3447 -10.03
as.factor(province)Favrskov -12.9033 1.4124 -9.14
as.factor(province)Faxe -9.2834 1.3816 -6.72
as.factor(province)Fredensborg -7.7790 1.2804 -6.08
as.factor(province)Fredericia 0.3388 1.2662 0.27
as.factor(province)Frederiksberg -1.9065 1.3424 -1.42
as.factor(province)Frederikshavn -7.6835 1.3802 -5.57
as.factor(province)Frederikssund -8.5687 1.3581 -6.31
as.factor(province)Furesø -7.4745 1.2415 -6.02
as.factor(province)Gentofte -4.6656 1.2507 -3.73
as.factor(province)Gladsaxe -3.9689 1.3726 -2.89
as.factor(province)Glostrup 6.7132 1.3281 5.05
as.factor(province)Greve -1.9669 1.2436 -1.58
as.factor(province)Gribskov -7.9928 1.3663 -5.85
as.factor(province)Guldborgsund -5.5136 1.3594 -4.06
as.factor(province)Haderslev -6.0824 1.2855 -4.73
as.factor(province)Halsnæs -6.5576 1.2694 -5.17
as.factor(province)Hedensted -12.2676 1.3935 -8.80
as.factor(province)Helsingør -1.4663 1.2380 -1.18
as.factor(province)Herlev 3.1992 1.3261 2.41
as.factor(province)Herning -7.3487 1.2752 -5.76
as.factor(province)Hillerød -4.5464 1.2483 -3.64
as.factor(province)Hjørring -8.8184 1.3760 -6.41
as.factor(province)Høje-Taastrup 2.3727 1.8210 1.30
as.factor(province)Holbæk -7.4408 1.3025 -5.71
as.factor(province)Holstebro -8.5188 1.3344 -6.38
as.factor(province)Horsens -5.2122 1.2380 -4.21
as.factor(province)Hørsholm -4.8924 1.2613 -3.88
as.factor(province)Hvidovre 0.0893 1.3433 0.07
as.factor(province)Ikast-Brande -0.7635 1.2646 -0.60
as.factor(province)Ishøj 7.6588 2.7607 2.77
as.factor(province)Jammerbugt -11.2642 1.4621 -7.70
as.factor(province)Kalundborg -7.7548 1.3924 -5.57
as.factor(province)Kerteminde -12.7600 1.3729 -9.29
as.factor(province)Køge -3.0168 1.2542 -2.41
as.factor(province)Kolding -3.1825 1.2490 -2.55
as.factor(province)Læsø -18.9533 1.4635 -12.95
as.factor(province)Langeland -12.5342 1.4098 -8.89
as.factor(province)Lejre -12.8366 1.4112 -9.10
as.factor(province)Lemvig -15.4707 1.4087 -10.98
as.factor(province)Lolland -1.3618 1.3449 -1.01
as.factor(province)Lyngby-Taarbæk -1.9291 1.2404 -1.56
as.factor(province)Mariagerfjord -9.7663 1.3868 -7.04
as.factor(province)Middelfart -9.2649 1.4011 -6.61
as.factor(province)Morsø -11.6821 1.4581 -8.01
as.factor(province)Næstved -7.3362 1.3286 -5.52
as.factor(province)Norddjurs -8.1579 1.3698 -5.96
as.factor(province)Nordfyns -11.1224 1.4042 -7.92
as.factor(province)Nyborg -6.0014 1.3447 -4.46
as.factor(province)Odder -13.2624 1.3741 -9.65
as.factor(province)Odense 1.0195 1.2618 0.81
as.factor(province)Odsherred -6.2749 1.4259 -4.40
as.factor(province)Randers -4.9908 1.3390 -3.73
as.factor(province)Rebild -13.1431 1.4669 -8.96
as.factor(province)Region Hovedstaden 4.2830 1.3496 3.17
as.factor(province)Region Midtjylland -6.3453 1.2690 -5.00
as.factor(province)Region Nordjylland -8.0186 1.3454 -5.96
as.factor(province)Region Sjælland -5.8666 1.3077 -4.49
as.factor(province)Region Syddanmark -5.2703 1.2626 -4.17
as.factor(province)Ringkøbing-Skjern -12.2451 1.3057 -9.38
as.factor(province)Ringsted -4.7622 1.2392 -3.84
as.factor(province)Rødovre 2.0717 1.3462 1.54
as.factor(province)Roskilde -4.7399 1.2723 -3.73
as.factor(province)Rudersdal -7.4063 1.2435 -5.96
as.factor(province)Samsø -16.7725 1.3405 -12.51
as.factor(province)Silkeborg -9.9554 1.3613 -7.31
as.factor(province)Skanderborg -12.2331 1.4095 -8.68
as.factor(province)Skive -11.2221 1.4149 -7.93
as.factor(province)Slagelse -1.0685 1.2615 -0.85
as.factor(province)Solrød -8.9423 1.3389 -6.68
as.factor(province)Sønderborg -7.9715 1.2381 -6.44
as.factor(province)Sorø -8.9418 1.4273 -6.26
as.factor(province)Stevns -14.0121 1.4068 -9.96
as.factor(province)Struer -11.4660 1.3247 -8.66
as.factor(province)Svendborg -9.2006 1.3522 -6.80
as.factor(province)Syddjurs -10.3702 1.3853 -7.49
as.factor(province)Tårnby 15.7099 1.2390 12.68
as.factor(province)Thisted -11.7390 1.3625 -8.62
as.factor(province)Tønder -6.9423 1.2760 -5.44
as.factor(province)Vallensbæk -5.7877 1.5524 -3.73
as.factor(province)Varde -9.5656 1.3222 -7.23
as.factor(province)Vejen -8.8759 1.3052 -6.80
as.factor(province)Vejle -5.5052 1.2501 -4.40
as.factor(province)Vesthimmerlands -8.0899 1.3499 -5.99
as.factor(province)Viborg -10.4339 1.3450 -7.76
as.factor(province)Vordingborg -7.7497 1.3959 -5.55
Pr(>|t|)
(Intercept) < 0.0000000000000002 ***
Foreign_pct 0.02975 *
as.factor(year)2009 0.14571
as.factor(year)2010 0.27220
as.factor(year)2011 0.43164
as.factor(year)2012 0.66200
as.factor(year)2013 0.0000050908852011 ***
as.factor(year)2014 0.01219 *
as.factor(year)2015 0.0000002125880600 ***
as.factor(year)2016 0.0000006260155570 ***
as.factor(year)2017 0.0000045612796564 ***
as.factor(year)2018 0.0000254026619226 ***
as.factor(year)2019 0.00020 ***
as.factor(year)2020 0.0000171391074557 ***
as.factor(year)2021 < 0.0000000000000002 ***
as.factor(year)2022 0.0000000000052288 ***
as.factor(year)2023 0.0000020652220260 ***
as.factor(province)Aalborg 0.00132 **
as.factor(province)Aarhus 0.12144
as.factor(province)Ærø < 0.0000000000000002 ***
as.factor(province)Albertslund 0.01233 *
as.factor(province)Allerød 0.00576 **
as.factor(province)Assens 0.0000000000000080 ***
as.factor(province)Ballerup 0.48977
as.factor(province)Billund 0.00037 ***
as.factor(province)Bornholm < 0.0000000000000002 ***
as.factor(province)Brøndby 0.01479 *
as.factor(province)Brønderslev < 0.0000000000000002 ***
as.factor(province)Christiansø < 0.0000000000000002 ***
as.factor(province)Copenhagen < 0.0000000000000002 ***
as.factor(province)Dragør < 0.0000000000000002 ***
as.factor(province)Egedal < 0.0000000000000002 ***
as.factor(province)Esbjerg 0.01017 *
as.factor(province)Faaborg-Midtfyn < 0.0000000000000002 ***
as.factor(province)Fanø < 0.0000000000000002 ***
as.factor(province)Favrskov < 0.0000000000000002 ***
as.factor(province)Faxe 0.0000000000255966 ***
as.factor(province)Fredensborg 0.0000000015562319 ***
as.factor(province)Fredericia 0.78910
as.factor(province)Frederiksberg 0.15575
as.factor(province)Frederikshavn 0.0000000305309908 ***
as.factor(province)Frederikssund 0.0000000003652042 ***
as.factor(province)Furesø 0.0000000021664788 ***
as.factor(province)Gentofte 0.00020 ***
as.factor(province)Gladsaxe 0.00389 **
as.factor(province)Glostrup 0.0000004824232486 ***
as.factor(province)Greve 0.11393
as.factor(province)Gribskov 0.0000000059925783 ***
as.factor(province)Guldborgsund 0.0000524437680822 ***
as.factor(province)Haderslev 0.0000024296828887 ***
as.factor(province)Halsnæs 0.0000002702897193 ***
as.factor(province)Hedensted < 0.0000000000000002 ***
as.factor(province)Helsingør 0.23643
as.factor(province)Herlev 0.01596 *
as.factor(province)Herning 0.0000000099794685 ***
as.factor(province)Hillerød 0.00028 ***
as.factor(province)Hjørring 0.0000000001946919 ***
as.factor(province)Høje-Taastrup 0.19279
as.factor(province)Holbæk 0.0000000133032162 ***
as.factor(province)Holstebro 0.0000000002276522 ***
as.factor(province)Horsens 0.0000269916243067 ***
as.factor(province)Hørsholm 0.00011 ***
as.factor(province)Hvidovre 0.94699
as.factor(province)Ikast-Brande 0.54611
as.factor(province)Ishøj 0.00560 **
as.factor(province)Jammerbugt 0.0000000000000234 ***
as.factor(province)Kalundborg 0.0000000300916854 ***
as.factor(province)Kerteminde < 0.0000000000000002 ***
as.factor(province)Køge 0.01627 *
as.factor(province)Kolding 0.01093 *
as.factor(province)Læsø < 0.0000000000000002 ***
as.factor(province)Langeland < 0.0000000000000002 ***
as.factor(province)Lejre < 0.0000000000000002 ***
as.factor(province)Lemvig < 0.0000000000000002 ***
as.factor(province)Lolland 0.31142
as.factor(province)Lyngby-Taarbæk 0.12011
as.factor(province)Mariagerfjord 0.0000000000028342 ***
as.factor(province)Middelfart 0.0000000000519225 ***
as.factor(province)Morsø 0.0000000000000022 ***
as.factor(province)Næstved 0.0000000393234871 ***
as.factor(province)Norddjurs 0.0000000032078438 ***
as.factor(province)Nordfyns 0.0000000000000045 ***
as.factor(province)Nyborg 0.0000086628385177 ***
as.factor(province)Odder < 0.0000000000000002 ***
as.factor(province)Odense 0.41922
as.factor(province)Odsherred 0.0000115371348300 ***
as.factor(province)Randers 0.00020 ***
as.factor(province)Rebild < 0.0000000000000002 ***
as.factor(province)Region Hovedstaden 0.00153 **
as.factor(province)Region Midtjylland 0.0000006374659755 ***
as.factor(province)Region Nordjylland 0.0000000031228935 ***
as.factor(province)Region Sjælland 0.0000077919824267 ***
as.factor(province)Region Syddanmark 0.0000315899951264 ***
as.factor(province)Ringkøbing-Skjern < 0.0000000000000002 ***
as.factor(province)Ringsted 0.00013 ***
as.factor(province)Rødovre 0.12401
as.factor(province)Roskilde 0.00020 ***
as.factor(province)Rudersdal 0.0000000031920660 ***
as.factor(province)Samsø < 0.0000000000000002 ***
as.factor(province)Silkeborg 0.0000000000004170 ***
as.factor(province)Skanderborg < 0.0000000000000002 ***
as.factor(province)Skive 0.0000000000000041 ***
as.factor(province)Slagelse 0.39714
as.factor(province)Solrød 0.0000000000334673 ***
as.factor(province)Sønderborg 0.0000000001607299 ***
as.factor(province)Sorø 0.0000000004826894 ***
as.factor(province)Stevns < 0.0000000000000002 ***
as.factor(province)Struer < 0.0000000000000002 ***
as.factor(province)Svendborg 0.0000000000144962 ***
as.factor(province)Syddjurs 0.0000000000001190 ***
as.factor(province)Tårnby < 0.0000000000000002 ***
as.factor(province)Thisted < 0.0000000000000002 ***
as.factor(province)Tønder 0.0000000615802463 ***
as.factor(province)Vallensbæk 0.00020 ***
as.factor(province)Varde 0.0000000000007310 ***
as.factor(province)Vejen 0.0000000000148711 ***
as.factor(province)Vejle 0.0000113768136880 ***
as.factor(province)Vesthimmerlands 0.0000000025626297 ***
as.factor(province)Viborg 0.0000000000000156 ***
as.factor(province)Vordingborg 0.0000000332443072 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.5 on 1544 degrees of freedom
Multiple R-squared: 0.767, Adjusted R-squared: 0.749
F-statistic: 42.7 on 119 and 1544 DF, p-value: <0.0000000000000002regression_std_df <- lm_to_std_df(lr2)Warning: longer object length is not a multiple of shorter object lengthWarning: longer object length is not a multiple of shorter object lengthwrite.csv(regression_std_df, 'regs/notfullcross.csv')lr3 <- lm(data=crosssec_long %>% filter(!dens==Inf), 'crime_rate ~ Foreign_pct + as.factor(year) + as.factor(province)')
summary(lr3)
Call:
lm(formula = "crime_rate ~ Foreign_pct + as.factor(year) + as.factor(province)",
data = crosssec_long %>% filter(!dens == Inf))
Residuals:
Min 1Q Median 3Q Max
-17.92 -1.42 -0.25 1.13 48.28
Coefficients:
Estimate Std. Error t value
(Intercept) 27.3345 1.3735 19.90
Foreign_pct -0.2198 0.1010 -2.18
as.factor(year)2009 0.7089 0.4870 1.46
as.factor(year)2010 -0.5374 0.4893 -1.10
as.factor(year)2011 -0.3875 0.4927 -0.79
as.factor(year)2012 -0.2177 0.4978 -0.44
as.factor(year)2013 -2.3108 0.5049 -4.58
as.factor(year)2014 -1.3004 0.5182 -2.51
as.factor(year)2015 -2.8036 0.5380 -5.21
as.factor(year)2016 -2.8745 0.5744 -5.00
as.factor(year)2017 -2.7789 0.6041 -4.60
as.factor(year)2018 -2.6583 0.6293 -4.22
as.factor(year)2019 -2.4205 0.6500 -3.72
as.factor(year)2020 -2.8514 0.6611 -4.31
as.factor(year)2021 -6.4674 0.6747 -9.59
as.factor(year)2022 -4.9142 0.7067 -6.95
as.factor(year)2023 -3.7000 0.7765 -4.77
as.factor(province)Aalborg -4.1037 1.2752 -3.22
as.factor(province)Aarhus 1.9805 1.2780 1.55
as.factor(province)Ærø -16.6342 1.3880 -11.98
as.factor(province)Albertslund 4.9439 1.9732 2.51
as.factor(province)Allerød 3.6558 1.3223 2.76
as.factor(province)Assens -11.2125 1.4293 -7.84
as.factor(province)Ballerup -0.8727 1.2633 -0.69
as.factor(province)Billund -4.5170 1.2656 -3.57
as.factor(province)Bornholm -14.0058 1.4219 -9.85
as.factor(province)Brøndby 5.2917 2.1686 2.44
as.factor(province)Brønderslev -12.5872 1.4285 -8.81
as.factor(province)Christiansø -24.0605 1.4706 -16.36
as.factor(province)Copenhagen 18.2720 1.6661 10.97
as.factor(province)Dragør -13.4478 1.3324 -10.09
as.factor(province)Egedal -12.2991 1.3220 -9.30
as.factor(province)Esbjerg -3.2719 1.2716 -2.57
as.factor(province)Faaborg-Midtfyn -12.2797 1.4203 -8.65
as.factor(province)Fanø -13.4867 1.3447 -10.03
as.factor(province)Favrskov -12.9033 1.4124 -9.14
as.factor(province)Faxe -9.2834 1.3816 -6.72
as.factor(province)Fredensborg -7.7790 1.2804 -6.08
as.factor(province)Fredericia 0.3388 1.2662 0.27
as.factor(province)Frederiksberg -1.9065 1.3424 -1.42
as.factor(province)Frederikshavn -7.6835 1.3802 -5.57
as.factor(province)Frederikssund -8.5687 1.3581 -6.31
as.factor(province)Furesø -7.4745 1.2415 -6.02
as.factor(province)Gentofte -4.6656 1.2507 -3.73
as.factor(province)Gladsaxe -3.9689 1.3726 -2.89
as.factor(province)Glostrup 6.7132 1.3281 5.05
as.factor(province)Greve -1.9669 1.2436 -1.58
as.factor(province)Gribskov -7.9928 1.3663 -5.85
as.factor(province)Guldborgsund -5.5136 1.3594 -4.06
as.factor(province)Haderslev -6.0824 1.2855 -4.73
as.factor(province)Halsnæs -6.5576 1.2694 -5.17
as.factor(province)Hedensted -12.2676 1.3935 -8.80
as.factor(province)Helsingør -1.4663 1.2380 -1.18
as.factor(province)Herlev 3.1992 1.3261 2.41
as.factor(province)Herning -7.3487 1.2752 -5.76
as.factor(province)Hillerød -4.5464 1.2483 -3.64
as.factor(province)Hjørring -8.8184 1.3760 -6.41
as.factor(province)Høje-Taastrup 2.3727 1.8210 1.30
as.factor(province)Holbæk -7.4408 1.3025 -5.71
as.factor(province)Holstebro -8.5188 1.3344 -6.38
as.factor(province)Horsens -5.2122 1.2380 -4.21
as.factor(province)Hørsholm -4.8924 1.2613 -3.88
as.factor(province)Hvidovre 0.0893 1.3433 0.07
as.factor(province)Ikast-Brande -0.7635 1.2646 -0.60
as.factor(province)Ishøj 7.6588 2.7607 2.77
as.factor(province)Jammerbugt -11.2642 1.4621 -7.70
as.factor(province)Kalundborg -7.7548 1.3924 -5.57
as.factor(province)Kerteminde -12.7600 1.3729 -9.29
as.factor(province)Køge -3.0168 1.2542 -2.41
as.factor(province)Kolding -3.1825 1.2490 -2.55
as.factor(province)Læsø -18.9533 1.4635 -12.95
as.factor(province)Langeland -12.5342 1.4098 -8.89
as.factor(province)Lejre -12.8366 1.4112 -9.10
as.factor(province)Lemvig -15.4707 1.4087 -10.98
as.factor(province)Lolland -1.3618 1.3449 -1.01
as.factor(province)Lyngby-Taarbæk -1.9291 1.2404 -1.56
as.factor(province)Mariagerfjord -9.7663 1.3868 -7.04
as.factor(province)Middelfart -9.2649 1.4011 -6.61
as.factor(province)Morsø -11.6821 1.4581 -8.01
as.factor(province)Næstved -7.3362 1.3286 -5.52
as.factor(province)Norddjurs -8.1579 1.3698 -5.96
as.factor(province)Nordfyns -11.1224 1.4042 -7.92
as.factor(province)Nyborg -6.0014 1.3447 -4.46
as.factor(province)Odder -13.2624 1.3741 -9.65
as.factor(province)Odense 1.0195 1.2618 0.81
as.factor(province)Odsherred -6.2749 1.4259 -4.40
as.factor(province)Randers -4.9908 1.3390 -3.73
as.factor(province)Rebild -13.1431 1.4669 -8.96
as.factor(province)Region Hovedstaden 4.2830 1.3496 3.17
as.factor(province)Region Midtjylland -6.3453 1.2690 -5.00
as.factor(province)Region Nordjylland -8.0186 1.3454 -5.96
as.factor(province)Region Sjælland -5.8666 1.3077 -4.49
as.factor(province)Region Syddanmark -5.2703 1.2626 -4.17
as.factor(province)Ringkøbing-Skjern -12.2451 1.3057 -9.38
as.factor(province)Ringsted -4.7622 1.2392 -3.84
as.factor(province)Rødovre 2.0717 1.3462 1.54
as.factor(province)Roskilde -4.7399 1.2723 -3.73
as.factor(province)Rudersdal -7.4063 1.2435 -5.96
as.factor(province)Samsø -16.7725 1.3405 -12.51
as.factor(province)Silkeborg -9.9554 1.3613 -7.31
as.factor(province)Skanderborg -12.2331 1.4095 -8.68
as.factor(province)Skive -11.2221 1.4149 -7.93
as.factor(province)Slagelse -1.0685 1.2615 -0.85
as.factor(province)Solrød -8.9423 1.3389 -6.68
as.factor(province)Sønderborg -7.9715 1.2381 -6.44
as.factor(province)Sorø -8.9418 1.4273 -6.26
as.factor(province)Stevns -14.0121 1.4068 -9.96
as.factor(province)Struer -11.4660 1.3247 -8.66
as.factor(province)Svendborg -9.2006 1.3522 -6.80
as.factor(province)Syddjurs -10.3702 1.3853 -7.49
as.factor(province)Tårnby 15.7099 1.2390 12.68
as.factor(province)Thisted -11.7390 1.3625 -8.62
as.factor(province)Tønder -6.9423 1.2760 -5.44
as.factor(province)Vallensbæk -5.7877 1.5524 -3.73
as.factor(province)Varde -9.5656 1.3222 -7.23
as.factor(province)Vejen -8.8759 1.3052 -6.80
as.factor(province)Vejle -5.5052 1.2501 -4.40
as.factor(province)Vesthimmerlands -8.0899 1.3499 -5.99
as.factor(province)Viborg -10.4339 1.3450 -7.76
as.factor(province)Vordingborg -7.7497 1.3959 -5.55
Pr(>|t|)
(Intercept) < 0.0000000000000002 ***
Foreign_pct 0.02975 *
as.factor(year)2009 0.14571
as.factor(year)2010 0.27220
as.factor(year)2011 0.43164
as.factor(year)2012 0.66200
as.factor(year)2013 0.0000050908852011 ***
as.factor(year)2014 0.01219 *
as.factor(year)2015 0.0000002125880600 ***
as.factor(year)2016 0.0000006260155570 ***
as.factor(year)2017 0.0000045612796564 ***
as.factor(year)2018 0.0000254026619226 ***
as.factor(year)2019 0.00020 ***
as.factor(year)2020 0.0000171391074557 ***
as.factor(year)2021 < 0.0000000000000002 ***
as.factor(year)2022 0.0000000000052288 ***
as.factor(year)2023 0.0000020652220260 ***
as.factor(province)Aalborg 0.00132 **
as.factor(province)Aarhus 0.12144
as.factor(province)Ærø < 0.0000000000000002 ***
as.factor(province)Albertslund 0.01233 *
as.factor(province)Allerød 0.00576 **
as.factor(province)Assens 0.0000000000000080 ***
as.factor(province)Ballerup 0.48977
as.factor(province)Billund 0.00037 ***
as.factor(province)Bornholm < 0.0000000000000002 ***
as.factor(province)Brøndby 0.01479 *
as.factor(province)Brønderslev < 0.0000000000000002 ***
as.factor(province)Christiansø < 0.0000000000000002 ***
as.factor(province)Copenhagen < 0.0000000000000002 ***
as.factor(province)Dragør < 0.0000000000000002 ***
as.factor(province)Egedal < 0.0000000000000002 ***
as.factor(province)Esbjerg 0.01017 *
as.factor(province)Faaborg-Midtfyn < 0.0000000000000002 ***
as.factor(province)Fanø < 0.0000000000000002 ***
as.factor(province)Favrskov < 0.0000000000000002 ***
as.factor(province)Faxe 0.0000000000255966 ***
as.factor(province)Fredensborg 0.0000000015562319 ***
as.factor(province)Fredericia 0.78910
as.factor(province)Frederiksberg 0.15575
as.factor(province)Frederikshavn 0.0000000305309908 ***
as.factor(province)Frederikssund 0.0000000003652042 ***
as.factor(province)Furesø 0.0000000021664788 ***
as.factor(province)Gentofte 0.00020 ***
as.factor(province)Gladsaxe 0.00389 **
as.factor(province)Glostrup 0.0000004824232486 ***
as.factor(province)Greve 0.11393
as.factor(province)Gribskov 0.0000000059925783 ***
as.factor(province)Guldborgsund 0.0000524437680822 ***
as.factor(province)Haderslev 0.0000024296828887 ***
as.factor(province)Halsnæs 0.0000002702897193 ***
as.factor(province)Hedensted < 0.0000000000000002 ***
as.factor(province)Helsingør 0.23643
as.factor(province)Herlev 0.01596 *
as.factor(province)Herning 0.0000000099794685 ***
as.factor(province)Hillerød 0.00028 ***
as.factor(province)Hjørring 0.0000000001946919 ***
as.factor(province)Høje-Taastrup 0.19279
as.factor(province)Holbæk 0.0000000133032162 ***
as.factor(province)Holstebro 0.0000000002276522 ***
as.factor(province)Horsens 0.0000269916243067 ***
as.factor(province)Hørsholm 0.00011 ***
as.factor(province)Hvidovre 0.94699
as.factor(province)Ikast-Brande 0.54611
as.factor(province)Ishøj 0.00560 **
as.factor(province)Jammerbugt 0.0000000000000234 ***
as.factor(province)Kalundborg 0.0000000300916854 ***
as.factor(province)Kerteminde < 0.0000000000000002 ***
as.factor(province)Køge 0.01627 *
as.factor(province)Kolding 0.01093 *
as.factor(province)Læsø < 0.0000000000000002 ***
as.factor(province)Langeland < 0.0000000000000002 ***
as.factor(province)Lejre < 0.0000000000000002 ***
as.factor(province)Lemvig < 0.0000000000000002 ***
as.factor(province)Lolland 0.31142
as.factor(province)Lyngby-Taarbæk 0.12011
as.factor(province)Mariagerfjord 0.0000000000028342 ***
as.factor(province)Middelfart 0.0000000000519225 ***
as.factor(province)Morsø 0.0000000000000022 ***
as.factor(province)Næstved 0.0000000393234871 ***
as.factor(province)Norddjurs 0.0000000032078438 ***
as.factor(province)Nordfyns 0.0000000000000045 ***
as.factor(province)Nyborg 0.0000086628385177 ***
as.factor(province)Odder < 0.0000000000000002 ***
as.factor(province)Odense 0.41922
as.factor(province)Odsherred 0.0000115371348300 ***
as.factor(province)Randers 0.00020 ***
as.factor(province)Rebild < 0.0000000000000002 ***
as.factor(province)Region Hovedstaden 0.00153 **
as.factor(province)Region Midtjylland 0.0000006374659755 ***
as.factor(province)Region Nordjylland 0.0000000031228935 ***
as.factor(province)Region Sjælland 0.0000077919824267 ***
as.factor(province)Region Syddanmark 0.0000315899951264 ***
as.factor(province)Ringkøbing-Skjern < 0.0000000000000002 ***
as.factor(province)Ringsted 0.00013 ***
as.factor(province)Rødovre 0.12401
as.factor(province)Roskilde 0.00020 ***
as.factor(province)Rudersdal 0.0000000031920660 ***
as.factor(province)Samsø < 0.0000000000000002 ***
as.factor(province)Silkeborg 0.0000000000004170 ***
as.factor(province)Skanderborg < 0.0000000000000002 ***
as.factor(province)Skive 0.0000000000000041 ***
as.factor(province)Slagelse 0.39714
as.factor(province)Solrød 0.0000000000334673 ***
as.factor(province)Sønderborg 0.0000000001607299 ***
as.factor(province)Sorø 0.0000000004826894 ***
as.factor(province)Stevns < 0.0000000000000002 ***
as.factor(province)Struer < 0.0000000000000002 ***
as.factor(province)Svendborg 0.0000000000144962 ***
as.factor(province)Syddjurs 0.0000000000001190 ***
as.factor(province)Tårnby < 0.0000000000000002 ***
as.factor(province)Thisted < 0.0000000000000002 ***
as.factor(province)Tønder 0.0000000615802463 ***
as.factor(province)Vallensbæk 0.00020 ***
as.factor(province)Varde 0.0000000000007310 ***
as.factor(province)Vejen 0.0000000000148711 ***
as.factor(province)Vejle 0.0000113768136880 ***
as.factor(province)Vesthimmerlands 0.0000000025626297 ***
as.factor(province)Viborg 0.0000000000000156 ***
as.factor(province)Vordingborg 0.0000000332443072 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.5 on 1544 degrees of freedom
Multiple R-squared: 0.767, Adjusted R-squared: 0.749
F-statistic: 42.7 on 119 and 1544 DF, p-value: <0.0000000000000002regression_std_df <- lm_to_std_df(lr3)Warning: longer object length is not a multiple of shorter object lengthWarning: longer object length is not a multiple of shorter object lengthwrite.csv(regression_std_df, 'regs/nosoc.csv')